Overview

Dataset statistics

Number of variables14
Number of observations20343
Missing cells11797
Missing cells (%)4.1%
Duplicate rows0
Duplicate rows (%)0.0%
Total size in memory2.2 MiB
Average record size in memory112.0 B

Variable types

Numeric11
Categorical3

Alerts

Name has a high cardinality: 19976 distinct valuesHigh cardinality
Mechanics has a high cardinality: 7381 distinct valuesHigh cardinality
ID is highly overall correlated with Year PublishedHigh correlation
Year Published is highly overall correlated with IDHigh correlation
Play Time is highly overall correlated with Complexity AverageHigh correlation
Users Rated is highly overall correlated with BGG Rank and 1 other fieldsHigh correlation
Rating Average is highly overall correlated with BGG Rank and 1 other fieldsHigh correlation
BGG Rank is highly overall correlated with Users Rated and 2 other fieldsHigh correlation
Complexity Average is highly overall correlated with Play Time and 1 other fieldsHigh correlation
Owned Users is highly overall correlated with Users Rated and 1 other fieldsHigh correlation
Mechanics has 1598 (7.9%) missing valuesMissing
Domains has 10159 (49.9%) missing valuesMissing
Max Players is highly skewed (γ1 = 43.49841135)Skewed
Play Time is highly skewed (γ1 = 73.63777821)Skewed
Name is uniformly distributedUniform
BGG Rank is uniformly distributedUniform
BGG Rank has unique valuesUnique
Play Time has 556 (2.7%) zerosZeros
Min Age has 1251 (6.1%) zerosZeros
Complexity Average has 426 (2.1%) zerosZeros

Reproduction

Analysis started2023-06-14 08:38:13.247724
Analysis finished2023-06-14 08:38:29.629861
Duration16.38 seconds
Software versionpandas-profiling v3.6.6
Download configurationconfig.json

Variables

ID
Real number (ℝ)

Distinct20327
Distinct (%)100.0%
Missing16
Missing (%)0.1%
Infinite0
Infinite (%)0.0%
Mean108216.25
Minimum1
Maximum331787
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size159.1 KiB
2023-06-14T10:38:29.725897image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile1365.6
Q111029
median88931
Q3192939.5
95-th percentile277168.1
Maximum331787
Range331786
Interquartile range (IQR)181910.5

Descriptive statistics

Standard deviation98682.097
Coefficient of variation (CV)0.91189726
Kurtosis-1.2733255
Mean108216.25
Median Absolute Deviation (MAD)82263
Skewness0.41644421
Sum2.1997116 × 109
Variance9.7381563 × 109
MonotonicityNot monotonic
2023-06-14T10:38:29.854073image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
174430 1
 
< 0.1%
39029 1
 
< 0.1%
12479 1
 
< 0.1%
120444 1
 
< 0.1%
11466 1
 
< 0.1%
158236 1
 
< 0.1%
12963 1
 
< 0.1%
220199 1
 
< 0.1%
174218 1
 
< 0.1%
7931 1
 
< 0.1%
Other values (20317) 20317
99.9%
(Missing) 16
 
0.1%
ValueCountFrequency (%)
1 1
< 0.1%
2 1
< 0.1%
3 1
< 0.1%
4 1
< 0.1%
5 1
< 0.1%
6 1
< 0.1%
7 1
< 0.1%
8 1
< 0.1%
9 1
< 0.1%
10 1
< 0.1%
ValueCountFrequency (%)
331787 1
< 0.1%
329465 1
< 0.1%
328871 1
< 0.1%
326624 1
< 0.1%
326485 1
< 0.1%
325635 1
< 0.1%
325555 1
< 0.1%
325494 1
< 0.1%
325022 1
< 0.1%
324345 1
< 0.1%

Name
Categorical

HIGH CARDINALITY  UNIFORM 

Distinct19976
Distinct (%)98.2%
Missing0
Missing (%)0.0%
Memory size159.1 KiB
Robin Hood
 
6
Gettysburg
 
4
Saga
 
4
Chaos
 
4
Cosmic Encounter
 
4
Other values (19971)
20321 

Length

Max length107
Median length85
Mean length18.434253
Min length1

Characters and Unicode

Total characters375008
Distinct characters268
Distinct categories19 ?
Distinct scripts8 ?
Distinct blocks14 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique19653 ?
Unique (%)96.6%

Sample

1st rowGloomhaven
2nd rowPandemic Legacy: Season 1
3rd rowBrass: Birmingham
4th rowTerraforming Mars
5th rowTwilight Imperium: Fourth Edition

Common Values

ValueCountFrequency (%)
Robin Hood 6
 
< 0.1%
Gettysburg 4
 
< 0.1%
Saga 4
 
< 0.1%
Chaos 4
 
< 0.1%
Cosmic Encounter 4
 
< 0.1%
Gangster 4
 
< 0.1%
Maya 3
 
< 0.1%
Kung Fu 3
 
< 0.1%
Polarity 3
 
< 0.1%
War of the Ring 3
 
< 0.1%
Other values (19966) 20305
99.8%

Length

2023-06-14T10:38:29.994108image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
the 3987
 
6.5%
of 2178
 
3.6%
game 1372
 
2.2%
885
 
1.4%
war 546
 
0.9%
edition 505
 
0.8%
in 475
 
0.8%
a 376
 
0.6%
card 369
 
0.6%
battle 341
 
0.6%
Other values (15953) 50156
82.0%

Most occurring characters

ValueCountFrequency (%)
40896
 
10.9%
e 33256
 
8.9%
a 27177
 
7.2%
o 21800
 
5.8%
r 21129
 
5.6%
i 20184
 
5.4%
n 18869
 
5.0%
t 18489
 
4.9%
s 15457
 
4.1%
l 13043
 
3.5%
Other values (258) 144708
38.6%

Most occurring categories

ValueCountFrequency (%)
Lowercase Letter 261681
69.8%
Uppercase Letter 55344
 
14.8%
Space Separator 40897
 
10.9%
Other Punctuation 8801
 
2.3%
Decimal Number 6406
 
1.7%
Dash Punctuation 1262
 
0.3%
Open Punctuation 238
 
0.1%
Close Punctuation 238
 
0.1%
Other Letter 100
 
< 0.1%
Math Symbol 12
 
< 0.1%
Other values (9) 29
 
< 0.1%

Most frequent character per category

Lowercase Letter
ValueCountFrequency (%)
e 33256
12.7%
a 27177
10.4%
o 21800
 
8.3%
r 21129
 
8.1%
i 20184
 
7.7%
n 18869
 
7.2%
t 18489
 
7.1%
s 15457
 
5.9%
l 13043
 
5.0%
h 9481
 
3.6%
Other values (82) 62796
24.0%
Other Letter
ValueCountFrequency (%)
6
 
6.0%
5
 
5.0%
3
 
3.0%
3
 
3.0%
3
 
3.0%
3
 
3.0%
3
 
3.0%
2
 
2.0%
2
 
2.0%
2
 
2.0%
Other values (58) 68
68.0%
Uppercase Letter
ValueCountFrequency (%)
T 5523
 
10.0%
S 4890
 
8.8%
C 4512
 
8.2%
B 3463
 
6.3%
G 3365
 
6.1%
D 3357
 
6.1%
M 3228
 
5.8%
A 3211
 
5.8%
W 2572
 
4.6%
P 2561
 
4.6%
Other values (43) 18662
33.7%
Other Punctuation
ValueCountFrequency (%)
: 4776
54.3%
! 1114
 
12.7%
' 1001
 
11.4%
, 573
 
6.5%
. 528
 
6.0%
& 502
 
5.7%
? 154
 
1.7%
/ 44
 
0.5%
\ 28
 
0.3%
" 26
 
0.3%
Other values (9) 55
 
0.6%
Decimal Number
ValueCountFrequency (%)
1 1695
26.5%
9 752
11.7%
4 743
11.6%
0 738
11.5%
2 549
 
8.6%
8 521
 
8.1%
5 429
 
6.7%
3 354
 
5.5%
6 319
 
5.0%
7 306
 
4.8%
Other Number
ValueCountFrequency (%)
2
40.0%
³ 1
20.0%
1
20.0%
² 1
20.0%
Math Symbol
ValueCountFrequency (%)
+ 10
83.3%
× 1
 
8.3%
1
 
8.3%
Currency Symbol
ValueCountFrequency (%)
$ 9
75.0%
2
 
16.7%
¥ 1
 
8.3%
Other Symbol
ValueCountFrequency (%)
1
33.3%
1
33.3%
° 1
33.3%
Space Separator
ValueCountFrequency (%)
40896
> 99.9%
  1
 
< 0.1%
Dash Punctuation
ValueCountFrequency (%)
- 1261
99.9%
1
 
0.1%
Nonspacing Mark
ValueCountFrequency (%)
́ 1
50.0%
1
50.0%
Open Punctuation
ValueCountFrequency (%)
( 238
100.0%
Close Punctuation
ValueCountFrequency (%)
) 238
100.0%
Connector Punctuation
ValueCountFrequency (%)
_ 2
100.0%
Modifier Symbol
ValueCountFrequency (%)
` 2
100.0%
Final Punctuation
ValueCountFrequency (%)
1
100.0%
Modifier Letter
ValueCountFrequency (%)
1
100.0%
Initial Punctuation
ValueCountFrequency (%)
1
100.0%

Most occurring scripts

ValueCountFrequency (%)
Latin 316972
84.5%
Common 57881
 
15.4%
Katakana 54
 
< 0.1%
Cyrillic 27
 
< 0.1%
Greek 26
 
< 0.1%
Han 25
 
< 0.1%
Hiragana 21
 
< 0.1%
Inherited 2
 
< 0.1%

Most frequent character per script

Latin
ValueCountFrequency (%)
e 33256
 
10.5%
a 27177
 
8.6%
o 21800
 
6.9%
r 21129
 
6.7%
i 20184
 
6.4%
n 18869
 
6.0%
t 18489
 
5.8%
s 15457
 
4.9%
l 13043
 
4.1%
h 9481
 
3.0%
Other values (104) 118087
37.3%
Common
ValueCountFrequency (%)
40896
70.7%
: 4776
 
8.3%
1 1695
 
2.9%
- 1261
 
2.2%
! 1114
 
1.9%
' 1001
 
1.7%
9 752
 
1.3%
4 743
 
1.3%
0 738
 
1.3%
, 573
 
1.0%
Other values (43) 4332
 
7.5%
Katakana
ValueCountFrequency (%)
6
 
11.1%
5
 
9.3%
3
 
5.6%
3
 
5.6%
3
 
5.6%
2
 
3.7%
2
 
3.7%
2
 
3.7%
2
 
3.7%
2
 
3.7%
Other values (21) 24
44.4%
Han
ValueCountFrequency (%)
1
 
4.0%
1
 
4.0%
1
 
4.0%
1
 
4.0%
1
 
4.0%
1
 
4.0%
1
 
4.0%
1
 
4.0%
1
 
4.0%
1
 
4.0%
Other values (15) 15
60.0%
Greek
ValueCountFrequency (%)
α 3
11.5%
ο 3
11.5%
λ 2
 
7.7%
μ 2
 
7.7%
ά 2
 
7.7%
ρ 2
 
7.7%
ε 2
 
7.7%
Ξ 1
 
3.8%
θ 1
 
3.8%
ι 1
 
3.8%
Other values (7) 7
26.9%
Cyrillic
ValueCountFrequency (%)
т 4
14.8%
и 4
14.8%
р 2
7.4%
у 2
7.4%
а 2
7.4%
С 2
7.4%
в 2
7.4%
к 2
7.4%
Э 2
7.4%
п 1
 
3.7%
Other values (4) 4
14.8%
Hiragana
ValueCountFrequency (%)
3
14.3%
3
14.3%
2
9.5%
2
9.5%
2
9.5%
2
9.5%
2
9.5%
1
 
4.8%
1
 
4.8%
1
 
4.8%
Other values (2) 2
9.5%
Inherited
ValueCountFrequency (%)
́ 1
50.0%
1
50.0%

Most occurring blocks

ValueCountFrequency (%)
ASCII 374120
99.8%
None 744
 
0.2%
Katakana 55
 
< 0.1%
Cyrillic 27
 
< 0.1%
CJK 25
 
< 0.1%
Hiragana 21
 
< 0.1%
Punctuation 8
 
< 0.1%
Currency Symbols 2
 
< 0.1%
Diacriticals 1
 
< 0.1%
VS 1
 
< 0.1%
Other values (4) 4
 
< 0.1%

Most frequent character per block

ASCII
ValueCountFrequency (%)
40896
 
10.9%
e 33256
 
8.9%
a 27177
 
7.3%
o 21800
 
5.8%
r 21129
 
5.6%
i 20184
 
5.4%
n 18869
 
5.0%
t 18489
 
4.9%
s 15457
 
4.1%
l 13043
 
3.5%
Other values (73) 143820
38.4%
None
ValueCountFrequency (%)
é 112
15.1%
ä 88
 
11.8%
ü 86
 
11.6%
ö 68
 
9.1%
ó 42
 
5.6%
á 35
 
4.7%
í 22
 
3.0%
ł 20
 
2.7%
ß 19
 
2.6%
à 19
 
2.6%
Other values (80) 233
31.3%
Katakana
ValueCountFrequency (%)
6
 
10.9%
5
 
9.1%
3
 
5.5%
3
 
5.5%
3
 
5.5%
2
 
3.6%
2
 
3.6%
2
 
3.6%
2
 
3.6%
2
 
3.6%
Other values (22) 25
45.5%
Punctuation
ValueCountFrequency (%)
4
50.0%
1
 
12.5%
1
 
12.5%
1
 
12.5%
1
 
12.5%
Cyrillic
ValueCountFrequency (%)
т 4
14.8%
и 4
14.8%
р 2
7.4%
у 2
7.4%
а 2
7.4%
С 2
7.4%
в 2
7.4%
к 2
7.4%
Э 2
7.4%
п 1
 
3.7%
Other values (4) 4
14.8%
Hiragana
ValueCountFrequency (%)
3
14.3%
3
14.3%
2
9.5%
2
9.5%
2
9.5%
2
9.5%
2
9.5%
1
 
4.8%
1
 
4.8%
1
 
4.8%
Other values (2) 2
9.5%
Currency Symbols
ValueCountFrequency (%)
2
100.0%
CJK
ValueCountFrequency (%)
1
 
4.0%
1
 
4.0%
1
 
4.0%
1
 
4.0%
1
 
4.0%
1
 
4.0%
1
 
4.0%
1
 
4.0%
1
 
4.0%
1
 
4.0%
Other values (15) 15
60.0%
Diacriticals
ValueCountFrequency (%)
́ 1
100.0%
VS
ValueCountFrequency (%)
1
100.0%
Misc Symbols
ValueCountFrequency (%)
1
100.0%
Letterlike Symbols
ValueCountFrequency (%)
1
100.0%
Math Operators
ValueCountFrequency (%)
1
100.0%
Latin Ext Additional
ValueCountFrequency (%)
1
100.0%

Year Published
Real number (ℝ)

Distinct188
Distinct (%)0.9%
Missing1
Missing (%)< 0.1%
Infinite0
Infinite (%)0.0%
Mean1984.2499
Minimum-3500
Maximum2022
Zeros185
Zeros (%)0.9%
Negative10
Negative (%)< 0.1%
Memory size159.1 KiB
2023-06-14T10:38:30.133073image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/

Quantile statistics

Minimum-3500
5-th percentile1975
Q12001
median2011
Q32016
95-th percentile2019
Maximum2022
Range5522
Interquartile range (IQR)15

Descriptive statistics

Standard deviation214.00318
Coefficient of variation (CV)0.10785092
Kurtosis142.7416
Mean1984.2499
Median Absolute Deviation (MAD)6
Skewness-10.992009
Sum40363611
Variance45797.362
MonotonicityNot monotonic
2023-06-14T10:38:30.253098image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
2017 1274
 
6.3%
2016 1257
 
6.2%
2018 1254
 
6.2%
2019 1134
 
5.6%
2015 1131
 
5.6%
2014 987
 
4.9%
2013 850
 
4.2%
2012 815
 
4.0%
2011 735
 
3.6%
2010 692
 
3.4%
Other values (178) 10213
50.2%
ValueCountFrequency (%)
-3500 1
 
< 0.1%
-3000 2
 
< 0.1%
-2600 1
 
< 0.1%
-2200 1
 
< 0.1%
-1400 2
 
< 0.1%
-1300 1
 
< 0.1%
-200 1
 
< 0.1%
-100 1
 
< 0.1%
0 185
0.9%
400 2
 
< 0.1%
ValueCountFrequency (%)
2022 1
 
< 0.1%
2021 144
 
0.7%
2020 684
3.4%
2019 1134
5.6%
2018 1254
6.2%
2017 1274
6.3%
2016 1257
6.2%
2015 1131
5.6%
2014 987
4.9%
2013 850
4.2%

Min Players
Real number (ℝ)

Distinct11
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean2.0197119
Minimum0
Maximum10
Zeros46
Zeros (%)0.2%
Negative0
Negative (%)0.0%
Memory size159.1 KiB
2023-06-14T10:38:30.360826image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile1
Q12
median2
Q32
95-th percentile3
Maximum10
Range10
Interquartile range (IQR)0

Descriptive statistics

Standard deviation0.69036572
Coefficient of variation (CV)0.34181395
Kurtosis10.937481
Mean2.0197119
Median Absolute Deviation (MAD)0
Skewness1.7359
Sum41087
Variance0.47660483
MonotonicityNot monotonic
2023-06-14T10:38:30.449838image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
Histogram with fixed size bins (bins=11)
ValueCountFrequency (%)
2 14076
69.2%
1 3270
 
16.1%
3 2365
 
11.6%
4 474
 
2.3%
5 57
 
0.3%
0 46
 
0.2%
6 21
 
0.1%
8 17
 
0.1%
7 14
 
0.1%
10 2
 
< 0.1%
ValueCountFrequency (%)
0 46
 
0.2%
1 3270
 
16.1%
2 14076
69.2%
3 2365
 
11.6%
4 474
 
2.3%
5 57
 
0.3%
6 21
 
0.1%
7 14
 
0.1%
8 17
 
0.1%
9 1
 
< 0.1%
ValueCountFrequency (%)
10 2
 
< 0.1%
9 1
 
< 0.1%
8 17
 
0.1%
7 14
 
0.1%
6 21
 
0.1%
5 57
 
0.3%
4 474
 
2.3%
3 2365
 
11.6%
2 14076
69.2%
1 3270
 
16.1%

Max Players
Real number (ℝ)

Distinct54
Distinct (%)0.3%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean5.6722214
Minimum0
Maximum999
Zeros161
Zeros (%)0.8%
Negative0
Negative (%)0.0%
Memory size159.1 KiB
2023-06-14T10:38:30.565836image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile2
Q14
median4
Q36
95-th percentile10
Maximum999
Range999
Interquartile range (IQR)2

Descriptive statistics

Standard deviation15.231376
Coefficient of variation (CV)2.6852576
Kurtosis2693.0695
Mean5.6722214
Median Absolute Deviation (MAD)2
Skewness43.498411
Sum115390
Variance231.99481
MonotonicityNot monotonic
2023-06-14T10:38:30.691838image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
4 6376
31.3%
2 4074
20.0%
6 3723
18.3%
5 2814
13.8%
8 1154
 
5.7%
10 380
 
1.9%
1 313
 
1.5%
7 309
 
1.5%
3 272
 
1.3%
12 236
 
1.2%
Other values (44) 692
 
3.4%
ValueCountFrequency (%)
0 161
 
0.8%
1 313
 
1.5%
2 4074
20.0%
3 272
 
1.3%
4 6376
31.3%
5 2814
13.8%
6 3723
18.3%
7 309
 
1.5%
8 1154
 
5.7%
9 73
 
0.4%
ValueCountFrequency (%)
999 3
 
< 0.1%
362 1
 
< 0.1%
200 1
 
< 0.1%
163 1
 
< 0.1%
127 1
 
< 0.1%
120 1
 
< 0.1%
100 14
 
0.1%
99 136
0.7%
75 2
 
< 0.1%
69 1
 
< 0.1%

Play Time
Real number (ℝ)

HIGH CORRELATION  SKEWED  ZEROS 

Distinct116
Distinct (%)0.6%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean91.294548
Minimum0
Maximum60000
Zeros556
Zeros (%)2.7%
Negative0
Negative (%)0.0%
Memory size159.1 KiB
2023-06-14T10:38:30.819870image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile10
Q130
median45
Q390
95-th percentile240
Maximum60000
Range60000
Interquartile range (IQR)60

Descriptive statistics

Standard deviation545.4472
Coefficient of variation (CV)5.9745868
Kurtosis7406.6538
Mean91.294548
Median Absolute Deviation (MAD)25
Skewness73.637778
Sum1857205
Variance297512.65
MonotonicityNot monotonic
2023-06-14T10:38:30.944837image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
30 3638
17.9%
60 3003
14.8%
45 2107
10.4%
20 2026
10.0%
120 1618
8.0%
90 1591
7.8%
15 1230
 
6.0%
180 805
 
4.0%
10 758
 
3.7%
0 556
 
2.7%
Other values (106) 3011
14.8%
ValueCountFrequency (%)
0 556
2.7%
1 23
 
0.1%
2 11
 
0.1%
3 3
 
< 0.1%
4 3
 
< 0.1%
5 138
 
0.7%
6 5
 
< 0.1%
7 2
 
< 0.1%
8 2
 
< 0.1%
10 758
3.7%
ValueCountFrequency (%)
60000 1
 
< 0.1%
22500 1
 
< 0.1%
17280 1
 
< 0.1%
14400 1
 
< 0.1%
12000 2
 
< 0.1%
10000 1
 
< 0.1%
8640 1
 
< 0.1%
7920 1
 
< 0.1%
6000 8
< 0.1%
5400 1
 
< 0.1%

Min Age
Real number (ℝ)

Distinct21
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean9.6014845
Minimum0
Maximum25
Zeros1251
Zeros (%)6.1%
Negative0
Negative (%)0.0%
Memory size159.1 KiB
2023-06-14T10:38:31.057683image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q18
median10
Q312
95-th percentile14
Maximum25
Range25
Interquartile range (IQR)4

Descriptive statistics

Standard deviation3.6454579
Coefficient of variation (CV)0.37967649
Kurtosis0.96300028
Mean9.6014845
Median Absolute Deviation (MAD)2
Skewness-0.85045773
Sum195323
Variance13.289364
MonotonicityNot monotonic
2023-06-14T10:38:31.303685image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
Histogram with fixed size bins (bins=21)
ValueCountFrequency (%)
12 4704
23.1%
8 4065
20.0%
10 3870
19.0%
14 1780
 
8.7%
0 1251
 
6.1%
13 1133
 
5.6%
6 917
 
4.5%
7 809
 
4.0%
5 436
 
2.1%
9 326
 
1.6%
Other values (11) 1052
 
5.2%
ValueCountFrequency (%)
0 1251
 
6.1%
1 2
 
< 0.1%
2 15
 
0.1%
3 114
 
0.6%
4 268
 
1.3%
5 436
 
2.1%
6 917
 
4.5%
7 809
 
4.0%
8 4065
20.0%
9 326
 
1.6%
ValueCountFrequency (%)
25 1
 
< 0.1%
21 11
 
0.1%
18 171
 
0.8%
17 59
 
0.3%
16 167
 
0.8%
15 147
 
0.7%
14 1780
 
8.7%
13 1133
 
5.6%
12 4704
23.1%
11 97
 
0.5%

Users Rated
Real number (ℝ)

Distinct2973
Distinct (%)14.6%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean840.97139
Minimum30
Maximum102214
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size159.1 KiB
2023-06-14T10:38:31.424204image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/

Quantile statistics

Minimum30
5-th percentile33
Q155
median120
Q3385
95-th percentile3158.9
Maximum102214
Range102184
Interquartile range (IQR)330

Descriptive statistics

Standard deviation3511.5622
Coefficient of variation (CV)4.1756025
Kurtosis223.41521
Mean840.97139
Median Absolute Deviation (MAD)80
Skewness12.353199
Sum17107881
Variance12331069
MonotonicityNot monotonic
2023-06-14T10:38:31.555243image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
30 271
 
1.3%
33 263
 
1.3%
31 262
 
1.3%
35 250
 
1.2%
32 248
 
1.2%
34 243
 
1.2%
39 239
 
1.2%
38 220
 
1.1%
41 217
 
1.1%
44 211
 
1.0%
Other values (2963) 17919
88.1%
ValueCountFrequency (%)
30 271
1.3%
31 262
1.3%
32 248
1.2%
33 263
1.3%
34 243
1.2%
35 250
1.2%
36 210
1.0%
37 207
1.0%
38 220
1.1%
39 239
1.2%
ValueCountFrequency (%)
102214 1
< 0.1%
101853 1
< 0.1%
101510 1
< 0.1%
84371 1
< 0.1%
78089 1
< 0.1%
71611 1
< 0.1%
67688 1
< 0.1%
64864 1
< 0.1%
63498 1
< 0.1%
63128 1
< 0.1%

Rating Average
Real number (ℝ)

Distinct622
Distinct (%)3.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean6.4032267
Minimum1.05
Maximum9.58
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size159.1 KiB
2023-06-14T10:38:31.683211image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/

Quantile statistics

Minimum1.05
5-th percentile4.82
Q15.82
median6.43
Q37.03
95-th percentile7.88
Maximum9.58
Range8.53
Interquartile range (IQR)1.21

Descriptive statistics

Standard deviation0.93591053
Coefficient of variation (CV)0.14616233
Kurtosis0.70059269
Mean6.4032267
Median Absolute Deviation (MAD)0.6
Skewness-0.29971779
Sum130260.84
Variance0.87592851
MonotonicityNot monotonic
2023-06-14T10:38:31.806243image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
6.5 110
 
0.5%
6.38 109
 
0.5%
6.43 107
 
0.5%
6.67 104
 
0.5%
6.72 104
 
0.5%
6.27 103
 
0.5%
6.28 103
 
0.5%
6.51 103
 
0.5%
6.53 102
 
0.5%
6.17 102
 
0.5%
Other values (612) 19296
94.9%
ValueCountFrequency (%)
1.05 1
< 0.1%
1.1 1
< 0.1%
1.32 1
< 0.1%
1.43 1
< 0.1%
1.5 1
< 0.1%
1.54 1
< 0.1%
1.55 1
< 0.1%
1.78 1
< 0.1%
1.9 1
< 0.1%
2.06 1
< 0.1%
ValueCountFrequency (%)
9.58 1
< 0.1%
9.54 1
< 0.1%
9.46 1
< 0.1%
9.43 2
< 0.1%
9.34 1
< 0.1%
9.31 1
< 0.1%
9.25 1
< 0.1%
9.24 2
< 0.1%
9.23 2
< 0.1%
9.22 1
< 0.1%

BGG Rank
Real number (ℝ)

HIGH CORRELATION  UNIFORM  UNIQUE 

Distinct20343
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean10172.89
Minimum1
Maximum20344
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size159.1 KiB
2023-06-14T10:38:31.935580image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile1018.1
Q15087.5
median10173
Q315258.5
95-th percentile19326.9
Maximum20344
Range20343
Interquartile range (IQR)10171

Descriptive statistics

Standard deviation5872.8316
Coefficient of variation (CV)0.57730216
Kurtosis-1.1999292
Mean10172.89
Median Absolute Deviation (MAD)5086
Skewness-7.7848833 × 10-5
Sum2.0694711 × 108
Variance34490151
MonotonicityStrictly increasing
2023-06-14T10:38:32.066591image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
1 1
 
< 0.1%
13562 1
 
< 0.1%
13569 1
 
< 0.1%
13568 1
 
< 0.1%
13567 1
 
< 0.1%
13566 1
 
< 0.1%
13565 1
 
< 0.1%
13564 1
 
< 0.1%
13563 1
 
< 0.1%
13561 1
 
< 0.1%
Other values (20333) 20333
> 99.9%
ValueCountFrequency (%)
1 1
< 0.1%
2 1
< 0.1%
3 1
< 0.1%
4 1
< 0.1%
5 1
< 0.1%
6 1
< 0.1%
7 1
< 0.1%
8 1
< 0.1%
9 1
< 0.1%
10 1
< 0.1%
ValueCountFrequency (%)
20344 1
< 0.1%
20343 1
< 0.1%
20342 1
< 0.1%
20341 1
< 0.1%
20340 1
< 0.1%
20339 1
< 0.1%
20338 1
< 0.1%
20337 1
< 0.1%
20336 1
< 0.1%
20335 1
< 0.1%

Complexity Average
Real number (ℝ)

HIGH CORRELATION  ZEROS 

Distinct379
Distinct (%)1.9%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean1.9911876
Minimum0
Maximum5
Zeros426
Zeros (%)2.1%
Negative0
Negative (%)0.0%
Memory size159.1 KiB
2023-06-14T10:38:32.200590image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile1
Q11.33
median1.97
Q32.54
95-th percentile3.529
Maximum5
Range5
Interquartile range (IQR)1.21

Descriptive statistics

Standard deviation0.84890322
Coefficient of variation (CV)0.4263301
Kurtosis0.012871143
Mean1.9911876
Median Absolute Deviation (MAD)0.63
Skewness0.41369357
Sum40506.73
Variance0.72063668
MonotonicityNot monotonic
2023-06-14T10:38:32.328640image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
1 2306
 
11.3%
2 1563
 
7.7%
1.5 692
 
3.4%
3 498
 
2.4%
1.33 448
 
2.2%
0 426
 
2.1%
1.67 407
 
2.0%
2.5 394
 
1.9%
2.33 296
 
1.5%
1.25 292
 
1.4%
Other values (369) 13021
64.0%
ValueCountFrequency (%)
0 426
 
2.1%
1 2306
11.3%
1.01 1
 
< 0.1%
1.02 12
 
0.1%
1.03 22
 
0.1%
1.04 28
 
0.1%
1.05 53
 
0.3%
1.06 55
 
0.3%
1.07 48
 
0.2%
1.08 66
 
0.3%
ValueCountFrequency (%)
5 1
< 0.1%
4.93 1
< 0.1%
4.91 2
< 0.1%
4.9 1
< 0.1%
4.89 2
< 0.1%
4.86 1
< 0.1%
4.84 1
< 0.1%
4.8 1
< 0.1%
4.78 1
< 0.1%
4.77 1
< 0.1%

Owned Users
Real number (ℝ)

Distinct3997
Distinct (%)19.7%
Missing23
Missing (%)0.1%
Infinite0
Infinite (%)0.0%
Mean1408.4576
Minimum0
Maximum155312
Zeros1
Zeros (%)< 0.1%
Negative0
Negative (%)0.0%
Memory size159.1 KiB
2023-06-14T10:38:32.457622image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile65
Q1146
median309
Q3864
95-th percentile5240.4
Maximum155312
Range155312
Interquartile range (IQR)718

Descriptive statistics

Standard deviation5040.1793
Coefficient of variation (CV)3.5785097
Kurtosis231.0049
Mean1408.4576
Median Absolute Deviation (MAD)208
Skewness12.312356
Sum28619859
Variance25403408
MonotonicityNot monotonic
2023-06-14T10:38:32.587587image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
122 67
 
0.3%
80 66
 
0.3%
109 63
 
0.3%
105 63
 
0.3%
73 63
 
0.3%
106 63
 
0.3%
103 62
 
0.3%
119 61
 
0.3%
100 60
 
0.3%
104 59
 
0.3%
Other values (3987) 19693
96.8%
ValueCountFrequency (%)
0 1
 
< 0.1%
3 4
< 0.1%
4 1
 
< 0.1%
5 3
< 0.1%
6 7
< 0.1%
7 2
 
< 0.1%
9 2
 
< 0.1%
10 6
< 0.1%
11 2
 
< 0.1%
12 7
< 0.1%
ValueCountFrequency (%)
155312 1
< 0.1%
154531 1
< 0.1%
149337 1
< 0.1%
112410 1
< 0.1%
107682 1
< 0.1%
101839 1
< 0.1%
97463 1
< 0.1%
94343 1
< 0.1%
92896 1
< 0.1%
87099 1
< 0.1%

Mechanics
Categorical

HIGH CARDINALITY  MISSING 

Distinct7381
Distinct (%)39.4%
Missing1598
Missing (%)7.9%
Memory size159.1 KiB
Hand Management
 
432
Hexagon Grid
 
412
Dice Rolling
 
372
Roll / Spin and Move
 
369
Tile Placement
 
285
Other values (7376)
16875 

Length

Max length406
Median length241
Mean length51.119605
Min length4

Characters and Unicode

Total characters958237
Distinct characters56
Distinct categories5 ?
Distinct scripts2 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique5866 ?
Unique (%)31.3%

Sample

1st rowAction Queue, Action Retrieval, Campaign / Battle Card Driven, Card Play Conflict Resolution, Communication Limits, Cooperative Game, Deck Construction, Deck Bag and Pool Building, Grid Movement, Hand Management, Hexagon Grid, Legacy Game, Modular Board, Once-Per-Game Abilities, Scenario / Mission / Campaign Game, Simultaneous Action Selection, Solo / Solitaire Game, Storytelling, Variable Player Powers
2nd rowAction Points, Cooperative Game, Hand Management, Legacy Game, Point to Point Movement, Set Collection, Trading, Variable Player Powers
3rd rowHand Management, Income, Loans, Market, Network and Route Building, Score-and-Reset Game, Tech Trees / Tech Tracks, Turn Order: Stat-Based, Variable Set-up
4th rowCard Drafting, Drafting, End Game Bonuses, Hand Management, Hexagon Grid, Income, Set Collection, Solo / Solitaire Game, Take That, Tile Placement, Turn Order: Progressive, Variable Player Powers
5th rowAction Drafting, Area Majority / Influence, Area-Impulse, Dice Rolling, Follow, Grid Movement, Hexagon Grid, Modular Board, Trading, Variable Phase Order, Variable Player Powers, Voting

Common Values

ValueCountFrequency (%)
Hand Management 432
 
2.1%
Hexagon Grid 412
 
2.0%
Dice Rolling 372
 
1.8%
Roll / Spin and Move 369
 
1.8%
Tile Placement 285
 
1.4%
Dice Rolling, Hexagon Grid, Simulation 260
 
1.3%
Dice Rolling, Hexagon Grid 256
 
1.3%
Set Collection 231
 
1.1%
Hand Management, Set Collection 179
 
0.9%
Pattern Recognition 155
 
0.8%
Other values (7371) 15794
77.6%
(Missing) 1598
 
7.9%

Length

2023-06-14T10:38:32.740588image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
dice 5728
 
4.4%
rolling 5672
 
4.3%
5185
 
4.0%
movement 4247
 
3.2%
hand 4152
 
3.2%
management 4152
 
3.2%
grid 3954
 
3.0%
game 3728
 
2.8%
and 3675
 
2.8%
player 3223
 
2.5%
Other values (267) 87508
66.7%

Most occurring characters

ValueCountFrequency (%)
112479
 
11.7%
e 87765
 
9.2%
n 70821
 
7.4%
a 67340
 
7.0%
i 67130
 
7.0%
o 60532
 
6.3%
t 50845
 
5.3%
l 50080
 
5.2%
r 40395
 
4.2%
, 38055
 
4.0%
Other values (46) 312795
32.6%

Most occurring categories

ValueCountFrequency (%)
Lowercase Letter 672960
70.2%
Uppercase Letter 124158
 
13.0%
Space Separator 112479
 
11.7%
Other Punctuation 44583
 
4.7%
Dash Punctuation 4057
 
0.4%

Most frequent character per category

Lowercase Letter
ValueCountFrequency (%)
e 87765
13.0%
n 70821
10.5%
a 67340
10.0%
i 67130
10.0%
o 60532
9.0%
t 50845
7.6%
l 50080
 
7.4%
r 40395
 
6.0%
d 25846
 
3.8%
c 25105
 
3.7%
Other values (15) 127101
18.9%
Uppercase Letter
ValueCountFrequency (%)
P 17542
14.1%
M 14580
11.7%
S 12947
10.4%
D 10430
8.4%
R 10056
8.1%
C 9208
7.4%
B 8037
6.5%
G 7726
 
6.2%
A 6996
 
5.6%
H 6942
 
5.6%
Other values (15) 19694
15.9%
Other Punctuation
ValueCountFrequency (%)
, 38055
85.4%
/ 6282
 
14.1%
: 233
 
0.5%
' 13
 
< 0.1%
Space Separator
ValueCountFrequency (%)
112479
100.0%
Dash Punctuation
ValueCountFrequency (%)
- 4057
100.0%

Most occurring scripts

ValueCountFrequency (%)
Latin 797118
83.2%
Common 161119
 
16.8%

Most frequent character per script

Latin
ValueCountFrequency (%)
e 87765
 
11.0%
n 70821
 
8.9%
a 67340
 
8.4%
i 67130
 
8.4%
o 60532
 
7.6%
t 50845
 
6.4%
l 50080
 
6.3%
r 40395
 
5.1%
d 25846
 
3.2%
c 25105
 
3.1%
Other values (40) 251259
31.5%
Common
ValueCountFrequency (%)
112479
69.8%
, 38055
 
23.6%
/ 6282
 
3.9%
- 4057
 
2.5%
: 233
 
0.1%
' 13
 
< 0.1%

Most occurring blocks

ValueCountFrequency (%)
ASCII 958237
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
112479
 
11.7%
e 87765
 
9.2%
n 70821
 
7.4%
a 67340
 
7.0%
i 67130
 
7.0%
o 60532
 
6.3%
t 50845
 
5.3%
l 50080
 
5.2%
r 40395
 
4.2%
, 38055
 
4.0%
Other values (46) 312795
32.6%

Domains
Categorical

Distinct39
Distinct (%)0.4%
Missing10159
Missing (%)49.9%
Memory size159.1 KiB
Wargames
3029 
Strategy Games
1455 
Family Games
1340 
Abstract Games
869 
Children's Games
708 
Other values (34)
2783 

Length

Max length46
Median length44
Mean length14.089258
Min length8

Characters and Unicode

Total characters143485
Distinct characters29
Distinct categories4 ?
Distinct scripts2 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique8 ?
Unique (%)0.1%

Sample

1st rowStrategy Games, Thematic Games
2nd rowStrategy Games, Thematic Games
3rd rowStrategy Games
4th rowStrategy Games
5th rowStrategy Games, Thematic Games

Common Values

ValueCountFrequency (%)
Wargames 3029
 
14.9%
Strategy Games 1455
 
7.2%
Family Games 1340
 
6.6%
Abstract Games 869
 
4.3%
Children's Games 708
 
3.5%
Thematic Games 647
 
3.2%
Party Games 409
 
2.0%
Family Games, Strategy Games 354
 
1.7%
Customizable Games 235
 
1.2%
Strategy Games, Thematic Games 217
 
1.1%
Other values (29) 921
 
4.5%
(Missing) 10159
49.9%

Length

2023-06-14T10:38:32.879622image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
games 8373
41.7%
wargames 3316
 
16.5%
strategy 2205
 
11.0%
family 2173
 
10.8%
thematic 1174
 
5.9%
abstract 1070
 
5.3%
children's 849
 
4.2%
party 605
 
3.0%
customizable 297
 
1.5%

Most occurring characters

ValueCountFrequency (%)
a 22529
15.7%
e 16214
11.3%
m 15333
10.7%
s 13905
9.7%
9878
 
6.9%
t 8626
 
6.0%
G 8373
 
5.8%
r 8045
 
5.6%
g 5521
 
3.8%
y 4983
 
3.5%
Other values (19) 30078
21.0%

Most occurring categories

ValueCountFrequency (%)
Lowercase Letter 111191
77.5%
Uppercase Letter 20062
 
14.0%
Space Separator 9878
 
6.9%
Other Punctuation 2354
 
1.6%

Most frequent character per category

Lowercase Letter
ValueCountFrequency (%)
a 22529
20.3%
e 16214
14.6%
m 15333
13.8%
s 13905
12.5%
t 8626
 
7.8%
r 8045
 
7.2%
g 5521
 
5.0%
y 4983
 
4.5%
i 4493
 
4.0%
l 3319
 
3.0%
Other values (8) 8223
 
7.4%
Uppercase Letter
ValueCountFrequency (%)
G 8373
41.7%
W 3316
 
16.5%
S 2205
 
11.0%
F 2173
 
10.8%
T 1174
 
5.9%
C 1146
 
5.7%
A 1070
 
5.3%
P 605
 
3.0%
Other Punctuation
ValueCountFrequency (%)
, 1505
63.9%
' 849
36.1%
Space Separator
ValueCountFrequency (%)
9878
100.0%

Most occurring scripts

ValueCountFrequency (%)
Latin 131253
91.5%
Common 12232
 
8.5%

Most frequent character per script

Latin
ValueCountFrequency (%)
a 22529
17.2%
e 16214
12.4%
m 15333
11.7%
s 13905
10.6%
t 8626
 
6.6%
G 8373
 
6.4%
r 8045
 
6.1%
g 5521
 
4.2%
y 4983
 
3.8%
i 4493
 
3.4%
Other values (16) 23231
17.7%
Common
ValueCountFrequency (%)
9878
80.8%
, 1505
 
12.3%
' 849
 
6.9%

Most occurring blocks

ValueCountFrequency (%)
ASCII 143485
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
a 22529
15.7%
e 16214
11.3%
m 15333
10.7%
s 13905
9.7%
9878
 
6.9%
t 8626
 
6.0%
G 8373
 
5.8%
r 8045
 
5.6%
g 5521
 
3.8%
y 4983
 
3.5%
Other values (19) 30078
21.0%

Interactions

2023-06-14T10:38:27.740557image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2023-06-14T10:38:14.735478image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2023-06-14T10:38:16.121478image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2023-06-14T10:38:17.375478image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2023-06-14T10:38:18.699478image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2023-06-14T10:38:19.945301image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2023-06-14T10:38:21.216884image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2023-06-14T10:38:22.556285image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2023-06-14T10:38:23.840180image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2023-06-14T10:38:25.058963image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2023-06-14T10:38:26.501557image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2023-06-14T10:38:27.863008image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2023-06-14T10:38:14.944477image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2023-06-14T10:38:16.236479image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2023-06-14T10:38:17.501479image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2023-06-14T10:38:18.815495image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2023-06-14T10:38:20.061331image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2023-06-14T10:38:21.345899image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2023-06-14T10:38:22.668328image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2023-06-14T10:38:23.950026image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2023-06-14T10:38:25.180964image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2023-06-14T10:38:26.616557image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2023-06-14T10:38:27.985016image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2023-06-14T10:38:15.062482image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2023-06-14T10:38:16.347479image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2023-06-14T10:38:17.614480image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2023-06-14T10:38:18.938514image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2023-06-14T10:38:20.173333image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2023-06-14T10:38:21.459850image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2023-06-14T10:38:22.780312image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2023-06-14T10:38:24.061017image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2023-06-14T10:38:25.298386image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2023-06-14T10:38:26.727555image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2023-06-14T10:38:28.102011image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2023-06-14T10:38:15.175480image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2023-06-14T10:38:16.457491image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2023-06-14T10:38:17.731511image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2023-06-14T10:38:19.048512image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2023-06-14T10:38:20.286298image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2023-06-14T10:38:21.567883image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2023-06-14T10:38:22.889315image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2023-06-14T10:38:24.170002image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2023-06-14T10:38:25.416453image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2023-06-14T10:38:26.835562image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2023-06-14T10:38:28.221007image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2023-06-14T10:38:15.289478image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2023-06-14T10:38:16.567478image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2023-06-14T10:38:17.841516image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2023-06-14T10:38:19.156481image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2023-06-14T10:38:20.399298image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2023-06-14T10:38:21.773850image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2023-06-14T10:38:23.000315image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2023-06-14T10:38:24.279024image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2023-06-14T10:38:25.534413image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2023-06-14T10:38:26.944557image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2023-06-14T10:38:28.345008image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2023-06-14T10:38:15.408479image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2023-06-14T10:38:16.681478image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2023-06-14T10:38:18.030478image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2023-06-14T10:38:19.271300image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2023-06-14T10:38:20.515333image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2023-06-14T10:38:21.886883image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2023-06-14T10:38:23.114283image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2023-06-14T10:38:24.394027image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2023-06-14T10:38:25.656451image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2023-06-14T10:38:27.059560image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2023-06-14T10:38:28.464008image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2023-06-14T10:38:15.522479image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2023-06-14T10:38:16.792478image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2023-06-14T10:38:18.135477image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2023-06-14T10:38:19.380331image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2023-06-14T10:38:20.627331image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2023-06-14T10:38:21.993884image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2023-06-14T10:38:23.264281image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2023-06-14T10:38:24.502025image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2023-06-14T10:38:25.772413image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2023-06-14T10:38:27.168559image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2023-06-14T10:38:28.583541image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2023-06-14T10:38:15.637477image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2023-06-14T10:38:16.904512image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2023-06-14T10:38:18.247476image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2023-06-14T10:38:19.490335image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2023-06-14T10:38:20.743297image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2023-06-14T10:38:22.102528image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2023-06-14T10:38:23.377315image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2023-06-14T10:38:24.611040image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2023-06-14T10:38:25.893557image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2023-06-14T10:38:27.282593image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2023-06-14T10:38:28.700507image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2023-06-14T10:38:15.748478image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2023-06-14T10:38:17.013477image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2023-06-14T10:38:18.352476image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2023-06-14T10:38:19.595332image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2023-06-14T10:38:20.853646image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2023-06-14T10:38:22.209513image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2023-06-14T10:38:23.484312image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2023-06-14T10:38:24.714963image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2023-06-14T10:38:26.007591image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2023-06-14T10:38:27.389591image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2023-06-14T10:38:28.828537image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2023-06-14T10:38:15.871514image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2023-06-14T10:38:17.136477image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2023-06-14T10:38:18.472481image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2023-06-14T10:38:19.715351image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2023-06-14T10:38:20.979646image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2023-06-14T10:38:22.327314image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2023-06-14T10:38:23.606279image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2023-06-14T10:38:24.830963image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2023-06-14T10:38:26.134557image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2023-06-14T10:38:27.508555image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2023-06-14T10:38:28.947537image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2023-06-14T10:38:15.996478image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2023-06-14T10:38:17.251479image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2023-06-14T10:38:18.581478image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2023-06-14T10:38:19.825300image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2023-06-14T10:38:21.091883image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2023-06-14T10:38:22.435312image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2023-06-14T10:38:23.716313image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2023-06-14T10:38:24.939964image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2023-06-14T10:38:26.372557image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2023-06-14T10:38:27.619587image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/

Correlations

2023-06-14T10:38:32.992588image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
IDYear PublishedMin PlayersMax PlayersPlay TimeMin AgeUsers RatedRating AverageBGG RankComplexity AverageOwned UsersDomains
ID1.0000.946-0.1400.045-0.1010.079-0.0230.398-0.225-0.059-0.0130.128
Year Published0.9461.000-0.1330.059-0.0630.1200.0540.423-0.286-0.0200.0660.098
Min Players-0.140-0.1331.0000.316-0.094-0.014-0.014-0.2080.115-0.192-0.0560.198
Max Players0.0450.0590.3161.000-0.090-0.0120.074-0.1880.083-0.2980.0070.024
Play Time-0.101-0.063-0.094-0.0901.0000.4660.1330.362-0.2660.6690.2110.000
Min Age0.0790.120-0.014-0.0120.4661.0000.1650.282-0.2530.4330.2240.370
Users Rated-0.0230.054-0.0140.0740.1330.1651.0000.250-0.7100.1720.9270.043
Rating Average0.3980.423-0.208-0.1880.3620.2820.2501.000-0.7660.5070.2730.383
BGG Rank-0.225-0.2860.1150.083-0.266-0.253-0.710-0.7661.000-0.400-0.6890.235
Complexity Average-0.059-0.020-0.192-0.2980.6690.4330.1720.507-0.4001.0000.2380.325
Owned Users-0.0130.066-0.0560.0070.2110.2240.9270.273-0.6890.2381.0000.059
Domains0.1280.0980.1980.0240.0000.3700.0430.3830.2350.3250.0591.000

Missing values

2023-06-14T10:38:29.125540image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
A simple visualization of nullity by column.
2023-06-14T10:38:29.341897image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
Nullity matrix is a data-dense display which lets you quickly visually pick out patterns in data completion.
2023-06-14T10:38:29.534862image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
The correlation heatmap measures nullity correlation: how strongly the presence or absence of one variable affects the presence of another.

Sample

IDNameYear PublishedMin PlayersMax PlayersPlay TimeMin AgeUsers RatedRating AverageBGG RankComplexity AverageOwned UsersMechanicsDomains
0174430.0Gloomhaven2017.01412014420558.7913.8668323.0Action Queue, Action Retrieval, Campaign / Battle Card Driven, Card Play Conflict Resolution, Communication Limits, Cooperative Game, Deck Construction, Deck Bag and Pool Building, Grid Movement, Hand Management, Hexagon Grid, Legacy Game, Modular Board, Once-Per-Game Abilities, Scenario / Mission / Campaign Game, Simultaneous Action Selection, Solo / Solitaire Game, Storytelling, Variable Player PowersStrategy Games, Thematic Games
1161936.0Pandemic Legacy: Season 12015.0246013416438.6122.8465294.0Action Points, Cooperative Game, Hand Management, Legacy Game, Point to Point Movement, Set Collection, Trading, Variable Player PowersStrategy Games, Thematic Games
2224517.0Brass: Birmingham2018.02412014192178.6633.9128785.0Hand Management, Income, Loans, Market, Network and Route Building, Score-and-Reset Game, Tech Trees / Tech Tracks, Turn Order: Stat-Based, Variable Set-upStrategy Games
3167791.0Terraforming Mars2016.01512012648648.4343.2487099.0Card Drafting, Drafting, End Game Bonuses, Hand Management, Hexagon Grid, Income, Set Collection, Solo / Solitaire Game, Take That, Tile Placement, Turn Order: Progressive, Variable Player PowersStrategy Games
4233078.0Twilight Imperium: Fourth Edition2017.03648014134688.7054.2216831.0Action Drafting, Area Majority / Influence, Area-Impulse, Dice Rolling, Follow, Grid Movement, Hexagon Grid, Modular Board, Trading, Variable Phase Order, Variable Player Powers, VotingStrategy Games, Thematic Games
5291457.0Gloomhaven: Jaws of the Lion2020.0141201483928.8763.5521609.0Action Queue, Campaign / Battle Card Driven, Communication Limits, Cooperative Game, Critical Hits and Failures, Deck Construction, Grid Movement, Hand Management, Hexagon Grid, Legacy Game, Line of Sight, Once-Per-Game Abilities, Scenario / Mission / Campaign Game, Simultaneous Action Selection, Solo / Solitaire Game, Variable Player PowersStrategy Games, Thematic Games
6182028.0Through the Ages: A New Story of Civilization2015.02412014230618.4374.4126985.0Action Points, Auction/Bidding, Auction: Dutch, Card Drafting, Events, Income, Take ThatStrategy Games
7220308.0Gaia Project2017.01415012163528.4984.3520312.0End Game Bonuses, Hexagon Grid, Income, Modular Board, Network and Route Building, Solo / Solitaire Game, Tech Trees / Tech Tracks, Turn Order: Pass Order, Variable Player Powers, Variable Set-up, Victory Points as a ResourceStrategy Games
8187645.0Star Wars: Rebellion2016.02424014230818.4293.7134849.0Area Majority / Influence, Area Movement, Area-Impulse, Delayed Purchase, Dice Rolling, Hand Management, Team-Based Game, Variable Player PowersThematic Games
912333.0Twilight Struggle2005.02218013408148.29103.5956219.0Action/Event, Advantage Token, Area Majority / Influence, Campaign / Battle Card Driven, Dice Rolling, Hand Management, Simulation, Simultaneous Action Selection, Sudden Death Ending, Tug of WarStrategy Games, Wargames
IDNameYear PublishedMin PlayersMax PlayersPlay TimeMin AgeUsers RatedRating AverageBGG RankComplexity AverageOwned UsersMechanicsDomains
203333737.0Operation1965.01610636174.10203351.116096.0SimulationChildren's Games
203343522.0LCR1983.031220518353.42203361.053441.0Dice RollingChildren's Games, Party Games
203351406.0Monopoly1933.0281808289994.39203371.6540255.0Auction/Bidding, Income, Loans, Lose a Turn, Player Elimination, Roll / Spin and Move, Set Collection, Stock Holding, Track Movement, TradingFamily Games
203362921.0The Game of Life1960.026608106584.30203381.1816692.0Roll / Spin and Move, SimulationFamily Games
203371410.0Trouble1965.02445432553.79203391.054962.0Roll / Spin and MoveChildren's Games
2033816398.0War0.02230413402.28203401.00427.0NaNChildren's Games
203397316.0Bingo1530.029960521542.85203411.051533.0Betting and Bluffing, Bingo, Pattern RecognitionParty Games
203405048.0Candy Land1949.02430340063.18203421.085788.0Roll / Spin and MoveChildren's Games
203415432.0Chutes and Ladders-200.02630337832.86203431.024400.0Dice Rolling, Grid Movement, Race, Roll / Spin and Move, Square GridChildren's Games
2034211901.0Tic-Tac-Toe-1300.0221432752.68203441.161374.0Paper-and-Pencil, Pattern BuildingAbstract Games, Children's Games